NBER WORKING PAPER SERIES

INEQUALITY AT BIRTH: SOME CAUSES AND CONSEQUENCES

Janet Currie

Working Paper 16798 http://www.nber.org/papers/w16798

NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2011

I am grateful to W. Bentley MacLeod for his advice and support and to the MacArthur Foundation and the Center for Health and Well Being at Princeton University for supporting this research. Douglas Almond, and seminar participants at the German Economic Association meetings for 2010, the Harvard Kennedy School and the University of Chicago’s Harris School provided helpful comments on early drafts. Samantha Heep, Katherine Meckel, and David Munroe provided outstanding research assistance. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

© 2011 by Janet Currie. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Inequality at Birth: Some Causes and Consequences Janet Currie NBER Working Paper No. 16798 February 2011 JEL No. I12,Q51,Q53

ABSTRACT

Recent research shows that health at birth is affected by many factors, including maternal education, behaviors, and participation in social programs. In turn, endowments at birth are predictive of adult outcomes, and of the outcomes of future generations. Exposure to environmental pollution is one potential determinant of health at birth that has received increasing attention. A large literature outside of economics advocates for “Environmental Justice,” and argues that poor and minority families are disproportionately exposed to environmental hazards. I provide new evidence on this question, showing that children born to less educated and minority mothers are more likely to be exposed to pollution in utero and that white, college educated mothers are particularly responsive to changes in environmental amenities. I estimate that differences in exposure to toxic releases may explain 6% of the gap in incidence of low birth weight between infants of white college educated mothers and infants of black high school dropout mothers.

Janet Currie International Affairs Building Department of Economics Columbia University - Mail code 3308 420 W 118th Street New York, NY 10027 and NBER [email protected]

Economists have long been interested in the origins of inequality between individuals and

between groups. Richard Ely, the founder of the American Economic Association and the person honored by these lectures, was certainly concerned about the “… share of the total

product of industry that is received by each section of the community” (Richard T. Ely, Thomas

S. Adams, Max O. Lorenz, Allyn A. Young, 1910, page 542). In his 1910 Outlines of

Economics, he concluded the chapter on the personal distribution of wealth with a call to action:

“Society must, therefore, take measures to better the environment of the poor. They must be taught to live wisely, and their children must be given a fair chance in life” (Ely, et al., 1910, page 550). In calling for reform, he explicitly rejected the view that poverty simply reflected the distribution of native abilities in the population and was therefore immutable.

Today, the same debate is often framed in terms of “nature vs. nurture.” Endowments at birth are thought of as representing “nature” and differences in achievements between similarly endowed groups reflect “nurture”. In this lecture, I will explore the possibility that differences that are often thought to be innate, may in fact reflect the effects of “nurture” or interactions between “nature and nurture,” and like Ely, I will focus on the importance of giving children a fair chance in life.

The first part of the lecture will review some of the evidence about the determinants of health at birth. I argue that individuals may start with very different endowments at birth because of events that happened to them during a critical period: The nine months that they were in utero. In turn, endowments at birth have been shown to be predictive of adult outcomes and of the outcomes of the next generation.

This focus on the prenatal period suggests that differences that appear to be innate may in fact be the product of environmental factors. While summarizing several influences on health at

2 birth, I will give considerable attention to one particular example of an environmental influence:

Prenatal exposure to pollution. A large literature outside of economics advocates for

“Environmental Justice,” arguing that poor and minority families are disproportionately exposed to environmental hazards. But issues of data quality and weaknesses in methodology leave this

assertion open to debate (William Bowen, 2002).

I provide new evidence on this question, showing that children born to less educated and

minority mothers are indeed more likely to be exposed to pollution in utero. The gradients in

pollution exposure by maternal race and education are clear cut when we use data at a fine

enough level of geographic disaggregation. More strikingly, I show that these gradients can arise

quickly following changes in environmental conditions and that white, college educated mothers

are particularly responsive to these changes.

These results shed light on some of the mechanisms underlying the perpetuation of lower

socioeconomic status. Poor and minority children are more likely to be in poor health at birth,

partly because their mothers are less able to provide a healthy fetal environment. Poor health at

birth is associated with poorer adult outcomes, which in turn provide less than optimal conditions

for the children of the poor. This conclusion suggests that policy makers attempting to

ameliorate inequalities among children cannot afford to ignore mothers, since what mothers do

even before they know they are pregnant may have profound consequences.

I. Endowments at Birth and Future Outcomes

This section provides an overview of the literature on health at birth with the aim of

establishing five important points: First, there are large and persistent inequalities in health at

birth. Second, the persistence of disparities cannot be taken as evidence that the source of

3 disparities is “genetic.” Indeed, the sharp distinction that is often made between “nature and

nurture” is now outdated and unhelpful. Third, health at birth is surprisingly malleable and

reflects the influences of a wide range of individual and social factors. Fourth, health at birth is a

useful predictor of important future outcomes such as earnings, education, and disability, though

the long-term effects of health at birth are themselves amenable to environmental influences.

Fifth, there is increasing evidence of intergenerational transmission of poor infant health at birth.

a) Endowments at Birth, Genes, and the Epigenome

Table 1 shows data calculated using singleton births from the U.S. National Individual-

Level Natality Data.1 This database contains information about virtually all of the approximately

4 million births per year in the U.S. The table illustrates huge inequality in health at birth. For

example, the incidence of low birth weight (birth weight less than 2500 grams) is more than

three times higher among children of black high school dropout mothers than among children of

white college educated mothers. Although this lecture will focus on low birth weight as the

measure of health at birth, disparities are also present if we look at alternative indicators such as

prematurity.2 I focus on birth weight because it has been measured over a long period of time

and is widely available. Moreover, while the limitations of birth weight as a summary measure

are increasingly well understood (see Douglas Almond, Kenneth Y. Chay, and David Lee, 2005),

little progress has been made towards finding an alternative, superior measure. Thus, for the

1 I focus on singleton births because multiple births are much more likely to be low birth weight and many multiple births result from Assisted Reproductive Technologies (ART). If one looks at all births, the fraction low birth weight among white college educated mothers has increased more than Table 1 suggests, because these mothers are more likely than others to use ART. 2 Disparities are also seen in APGAR scores. The APGAR score is a rating from zero to ten of the infant’s health 5 minutes after birth. Wanchuan Lin (2008) shows that while there was no convergence in the incidence of low birth weight from 1983 to 2000, gaps in APGAR scores and infant mortality declined, largely due to improvements in medical care at the time of child birth.

4 time being we must look under the lamp-post, while hoping that a better light source will soon

become available.

Table 1 indicates that differences in endowments at birth have been relatively stable over time. There has been a tendency to view this stability as indicative of group-level genetic

differences (Richard J. Herrnstein and Charles Murray, 1994). Yet the emerging science of

epigenetics suggests that a much subtler interplay of genes and the environment is at work.

Arturas Petronis (2010) argues that “it is difficult to visualize how highly stable DNA sequences

can account for heritability which is malleable and context-dependent” (page 722).

A puzzle brought to light by the sequencing of the human genome is that human beings

have so few genes - approximately 23,000, about the same number as a fish or a mouse. It

seems that there are too few genes to explain the complexity of humankind. Moreover, we now

know that unrelated individuals share over 99% of their DNA and that those genetic variations

(polymorphisms) that have been identified explain little of the variation observed in the

population (Lars Feuk, Andrew R. Carson, and Stephen W. Scherer, 2006).

For example, height, an important predictor of future outcomes, is strongly heritable; that

is, the height of children tends to resemble the height of their parents. Genome-wide association

studies (GWAS) have detected 40 areas of the DNA that affect height. However, variations in

these regions of the genome can explain less than 5% of the heritability of height in humans

(Brendan Maher, 2008).3 Moreover, economic historians have shown that the average height of

3 There is a great deal of controversy in genetics about the “missing heritability.” GWAS studies use stringent significance levels to avoid false positives. Yang et al. (2010) argue that taken as a group, variations in the genome (more technically, the approximately 300,000 single nucleotide polymorphisms, or SNPs that have been identified) can explain over half of the heritability of height, even if few single SNPs are significantly associated with height. However, statistically one might expect to be able to explain a large fraction of the variation in the variable of interest given such a large number of potential explanatory variables. The controversy about the

5 human populations can change rapidly with improvements in health and nutrition (Richard

Steckel, 1995). Anne Case and Christina Paxson (2008, 2010) argue that height is in fact an

indicator of early deprivation.

These puzzles suggest that genes cannot be the whole story, and much recent work in

genetics focuses on the epigenome, which literally means “above the genome.” The epigenome

determines which genes are expressed. It can be thought of as a series of switches that turn parts

of the genome on and off. Another metaphor is that it is the “software” that corresponds to the

genetic “hardware” and tells the “hardware” what to do. Perhaps the best example of epigenetics

at work is the development of an infant from a single cell. A person’s skin, blood and hair all

start from a single cell and have the same DNA. These tissues are differentiated as a series of

epigenetic switches are “thrown” over the course of development.

One process by which parts of the gene are expressed or silenced involves a methyl

molecule that attaches to a part of the DNA. The part of the DNA that is “methylated” is hidden

from the cell and is not expressed. Another process involves a chemical tag that attaches to the

histone, which is the protein that the DNA wraps around, and changes its shape, thus changing the functioning of the gene. These switches can be triggered by environmental factors, and changes in the arrangement of the switches can be passed from parents to their offspring.

Thus, epigenetics offers an elegant theory of how environmental factors can rapidly “get under the skin.” For example, some mice have a gene that causes them to have a yellow coat and to be prone to obesity and disease. If pregnant mice with this “agouti” gene are fed diets high in folic acid and B-12, their offspring are thin and brown (Craig A. Cooney, Apurva A. Dave, and

George L. Wolff, 2002). The diet enables mothers to create methyl molecules that attach to the

measurement of heritability in genetics makes the evidence regarding rapid changes in the height of human populations over time in response to environmental factors even more important.

6 agouti gene in key locations and “silence it” in their offspring. Experiments like these suggest

that the variation in human characteristics that we see results from complex interactions between

genes and the environment.

b) The Malleability of Health at Birth

We do not need to look for evidence at the molecular level to find evidence that health at

birth is malleable. Many recent studies in economics show that birth weight is affected by a

wide range of factors.4 Today it is perhaps unsurprising to hear that tobacco, alcohol, and illegal

drug use during pregnancy have negative effects, or that good nutrition and better access to

medical care have positive effects on fetal health. Damage due to fetal alcohol syndrome (FAS)

provides an instructive early example of a condition that was falsely attributed to “genetics” even though it was environmental in origin. The facial features and behaviors typical of FAS had been recognized for a long time but had been attributed to heredity. For example, Henry H.

Goddard’s (1912) monograph about the Kallilak family of “congenital idiots” was subtitled, “A

Study in the Heredity of Feeble-mindedness.” However, Robert J. Karp, et al. (1995) shows that

FAS offers an excellent explanation of the characteristics Goddard describes.

More recently, economists have helped to quantify the magnitude of these effects (Janet

Currie and Jonathan Gruber, 1996; William N. Evans, Jeanne S. Ringel, and Diana Stech, 1999;

Currie and Matthew Neidell, 2005; Kelly Noonan, et al., 2007; Currie, Neidell, and Johannes F.

Schmeider, 2009; Angela R. Fertig and Tara Watson, 2009; David S. Ludwig and Currie, 2010).

For example, Currie, Neidell, and Schmeider (2009) use confidential data from birth certificates

4 Almond and Currie (2010) and Currie (2009) offer more detailed overviews of factors that have been shown to influence birth weight, and of policies that have been shown to be effective in ameliorating the long-term consequences of low birth weight.

7 on 1.5 million births in New Jersey between 1989 and 2003 in which births to the same mother

can be linked. They compare births to the same mother in pairs in which the mother smoked

during one pregnancy but not during the other. These fixed effects estimates of negative effects

of smoking on birth weight are smaller than ordinary least squares estimates, but are still

substantial: At the mean number of cigarettes smoked per day (ten), they estimate that smoking

increases the probability of low birth weight by .018 percentage points on a baseline of .089

(compared to an Ordinary Least Squares estimate of .067 percentage points).

The introduction of social programs such as the Supplemental Feeding Program for

Women, Infants, and Children (WIC) and Food Stamps in the 1970s (Hilary W. Hoynes,

Marianne E. Page, and Ann Huff Stevens, 2009; Almond, Hoynes, and Diane Whitmore

Schanzenbach, forthcoming) have also been shown to affect birth weight. For example, Hoynes,

Page, and Stevens (2009) find that among mothers who were high school dropouts, and mothers in high poverty counties, the introduction of WIC reduced the proportion of births that were of lower birth weight by one to 2.5 percent.

Currie and Enrico Moretti (2003) investigate the effect of increases in maternal education on infant health outcomes. While there is a large literature on this topic, it is difficult to find an instrument that affects education without possibly having an independent effect on infant health.

Currie and Moretti (2003) use data on college openings in the woman’s county of birth in the year in which she turned 17. College openings are shown to have had a significant effect on the education of white mothers, though they had no effect on black mothers (who may have faced other constraints on college attendance) or men (who were presumably less geographically constrained). Children of women induced to attend college by the openings were significantly healthier: The estimates suggest that an additional year of college education reduces the

8 incidence of low birth weight by ten percent. These positive results may be because college education dramatically reduces smoking, increases the probability that a woman gets timely prenatal care, and increases the probability that she is married at the time of the birth.5

A fourth strand of the recent literature in economics examines the effects of pollution on health at birth. Cross-sectional differences in ambient pollution are usually correlated with other determinants of fetal health. For example, fetuses exposed to lower levels of pollution may also receive higher quality medical care. Failing to account for these relationships leads to upwardly biased estimates of the effects of pollution. Epidemiological studies typically have few (if any) controls for these potential confounders.6

Chay and Michael Greenstone (2003a, 2003b) address the problem of omitted variables

by focusing on "natural experiments" provided by the implementation of the Clean Air Act of

1970 and the recession of the early 1980s. Both the Clean Air Act and the recession induced

sharper reductions in air-borne particulates in some counties than in others, and they use this

exogenous variation in levels of air pollution at the county-year level to identify its effects. They

estimate that a one unit decline in particulates caused by the implementation of the Clean Air Act

(or recession) led to between five and eight (four and seven) fewer infant deaths per 100,000 live

births. They also find some evidence that the decline in Total Suspended Particles (TSPs) led to

5 Subsequent studies using laws affecting the compulsory schooling of high school educated mothers have not shown positive impacts on birth weight (Maarten Lindeboom, Ana Llena- Nozal, and Bas van der Klaauw, 2009; Justin McCrary and Heather Royer, forthcoming). Gabriella Conti, James J. Heckman, Hedibert Lopes, and Remi Piatek (forthcoming) reconcile these findings using data from the 1970 British Cohort Study and showing that the women most likely to select into higher education have higher returns to education in terms of both wages and smoking behavior. Clearly, one should be cautious about drawing inferences about the benefits of forcing would-be high school dropouts to stay in school from a study that focuses on the effects of giving women who wanted to go to college the opportunity to do so. 6 There are some important exceptions. For example, Jennifer Parker, Pauline Mendola, and Tracey Woodruff (2008) study a natural experiment caused by the closure and reopening of a pollutant emitting steel mill in a valley in Utah, and find that the closure reduced preterm birth.

9 reductions in the incidence of low birth weight. However, only TSPs were measured at that time, so that they could not study the effects of other pollutants. And the levels of particulates studied by Chay and Greenstone (2003a, 2003b) are much higher than those prevalent today; for example, PM10 (particulate matter of 10 microns or less) levels have fallen by nearly 50 percent from 1980 to 2000.

Several recent studies consider natural experiments at more recently-encountered pollution levels. For example, the Currie, Neidell, and Schmieder (2009) study discussed above focuses on a sample of mothers who lived near pollution monitors and shows that infants exposed in utero to higher levels of carbon monoxide (which comes largely from vehicle exhaust) suffered reduced birth weight and gestation length relative to siblings even though ambient CO levels were generally much lower than current Environmental Protection Agency

(EPA) standards. The estimates suggest that moving from an area with high levels of CO to one with low levels of CO would have an effect larger than getting a woman who was smoking ten cigarettes a day during pregnancy to quit!7 Moreover, CO exposure increases the risk of death among newborns by 2.5 percent. The negative effects of CO exposure are five times greater for smokers than for non-smokers, and there is some evidence of negative effects of exposure to ozone and particulates among infants of smokers. Katja Coneus and C. Katharina Spiess (2010) adopt similar methods using German data, and also find large effects of CO on infant health.

Currie and Reed Walker (2011) exploit the introduction of electronic toll collection devices (E-ZPass) in New Jersey and Pennsylvania. Since much of the pollution produced by

7 The standard for eight hour CO concentrations is nine parts per million (ppm). The mean in our sample is 1.6ppm, but some areas had levels around four. Moving from an area with 4ppm to one with 1ppm in the third trimester would reduce low birth weight by 2.5 percentage points, while going from ten to zero cigarettes per day would reduce the incidence of low birth weight by 1.8 percentage points.

10 automobiles occurs when idling or accelerating back to highway speed, electronic toll collection

greatly reduces auto emissions in the vicinity of a toll plaza. They compare mothers near toll

plazas to those who live near busy roadways but further from toll plazas and find that E-ZPass increases birth weight and gestation. They obtain similar estimates when they follow mothers over time and compare siblings born before and after adoption of E-ZPass. E-ZPass reduced CO by about 40 percent in the vicinity of toll plazas and also reduced concentrations of many other

pollutants found in vehicle exhaust. These reductions reduce the incidence of low birth weight

by about one percentage point in the two kilometers surrounding the toll plaza and by as much as

2.25 percentage points in areas immediately adjacent to the toll plaza.8

Currie and Schmeider (2009) focus on the effects of toxic emissions to the air as

measured by the Environmental Protection Agency’s Toxic Release Inventory (TRI), a program

discussed further below. They distinguish between chemicals that are known to affect the

developing fetus (developmental chemicals) and other toxins. They also distinguish between

“fugitive releases” and releases that go up a smoke stack. The latter are less likely to be harmful to the plant’s neighbors since stacks generally have “scrubbers” and disperse pollutants over a wide area. They find evidence of significant effects. For example, at the county level, a two standard deviation increase in releases of the heavy metal cadmium would increase the incidence of low birth weight by 1.2 percent, while a two standard deviation increase in emissions of toluene (a common volatile organic compound) would increase it 2.7 percent. Given that these

8 In contrast to the results reported below, they did not find any impact of E-ZPass adoption on the demographic composition of births in the immediate vicinity of the toll plazas in the three years before and after adoption. It is possible that mothers did not realize the health benefits associated with adoption since CO is an odorless, colorless gas, and the negative health effects of ambient CO levels on fetal health are a subject of current research and therefore not widely known.

11 are county-level average effects, it is likely that effects are much larger for those located close to

the emitting facilities, as discussed further below.

Together these studies demonstrate that health at birth is sensitive to many environmental

factors, including maternal behaviors like smoking (which in turn may be influenced by maternal

education) and maternal exposure to pollution. They also indicate that health at birth is sensitive

to maternal participation in social programs like WIC.

c) Long-run Consequences of Health at Birth

In addition to showing that health at birth is influenced by many environmental factors,

economists have been active in demonstrating that health at birth is predictive of future

outcomes. This point is apparent in Figure 1, which is constructed using data on the Children of

the National Longitudinal Survey of Youth (NLSY). These are children of the original NLSY members who were 14 to 21 in 1978, and their children are now young adults. Figure 1 shows, as others have also shown (see Currie and Duncan Thomas, 2001; Case and Paxson, 2008, 2010;

Flavio Cunha and Heckman, 2008; Raj Chetty, et al., 2010; Cunha, Heckman, and Susanne M.

Schennach, 2010), that indicators of human capital measured early in life are predictive of future outcomes.

What is more striking about Figure 1, is that the relationship between birth weight and future outcomes is almost as strong in this sample as the relationship between test scores and outcomes. Epidemiologists such as David J. P. Barker (1998) have shown associations between health at birth and future health, but have not focused on measures of “economic” outcomes such as earnings and college attendance.

12 The association shown in the figure does not necessarily represent a causal relationship.

Many omitted factors could be correlated both with negative birth outcomes and with lower future performance. Some of the most convincing studies indicating a causal relationship between health at birth and future outcomes use large national or state-level samples and show that within sibling or twin pairs, children of lower birth weight have worse adult outcomes in terms of schooling attainment, test scores, use of disability programs, residence in high income areas, and wages (Sandra E. Black, Paul J. Devereux, and Kjell G. Salvanes, 2007; Currie and

Moretti, 2007; Philip Oreopoulus, et al., 2008; Royer, 2009; Prashant Bharadwaj, Juan Eberhard, and Christopher Neilson, 2010; Currie, et al., 2010).

For example, extrapolated to the U.S., the estimates in Black, Devereux, and Salvanes

(2007) suggest that if the mean birth weight of high school educated women was increased to the mean birth weight of college educated mothers, the earnings of affected male children would increase by two percent, and the probability of high school graduation would increase by one percent among affected female children. It is important to note at this point that the long-term effects of low birth weight are themselves subject to environmental influence. Currie and

Moretti (2007) find that women who were low birth weight are more likely to be poor (proxied by residence in a low income zip code at the time of their own child’s birth) and have less education than other mothers. But most of this effect is accounted for by mothers who were also born in low income neighborhoods, and low birth weight has relatively little impact among women from better backgrounds.

Birth weight is the most widely available measure of fetal health, and is often treated as a summary measure. But there is reason to believe that it does not capture the full spectrum of fetal health effects (Almond, Chay, and Lee, 2005). One important issue is that the fetus

13 typically gains most of its weight in the third trimester, whereas studies often find that shocks in

the first trimester are particularly harmful. Thus, birth weight may not be a sensitive measure of things that happen during the most critical period of fetal development.

Direct investigations of the long term impacts of fetal health shocks often find large effects and enduring effects. For example, Almond, Lena Edlund, and Marten Palme (2009) study the fallout from the Chernobyl nuclear disaster. Radiation affected some areas of Sweden but not others. By examining cohorts in affected and unaffected areas, and cohorts in utero just prior to the disaster and during the disaster, they are able to show that radiation exposure reduced the probability that affected children qualified for high school by three percent and reduced mathematics test scores by six percent. This result is despite the fact that, at the time, the amounts of radiation involved were considered to be so low as to be completely harmless.

Paradoxically, the long-term effects of an event like Chernobyl may be easier to identify than the effects of more severe shocks. Studies of the impact of health shocks in utero or early in life can come to conflicting conclusions depending on how the shock affects the probability of conception and the probability of survival for high and low income people (Carlos Bozzoli,

Angus Deaton, and Climent Quintana-Domeque, 2009). Migration can also make measurement of the childhood conditions of older cohorts difficult.

Two recent studies, by Yuyu Chen and Li-An Zhou (2007) and Almond, et al. (2008) examine the long-term effects of the Chinese famine of 1959 to 1961 with designs that try to deal with these problems. Chen and Zhou (2007) rely on a small sample and pool children affected in utero with those affected in early childhood. They find evidence of significant negative effects on height, income, and labor supply. Almond, et al. (2008) use data from the Census, and find dramatic effects on children subjected to the famine in utero: affected men (women) were nine

14 percent (six percent) more likely to be illiterate and six percent (three percent) less likely to work. The finding of larger effects for men than for women is striking and not uncommon in the fetal effects literature. However, Robert S. Scholte, Gerard J. van den Berg, and Maarten

Lindeboom (2010) examine the long-term effects of the Dutch “hunger winter” that took place during the Nazi occupation of the Netherlands in 1945 and find evidence of effects on health, but not on income or disability. They argue that long-term effects may be obscured by selection since the probability of surviving is lower for cohorts affected by the famine.

Almond (2006) studies the long run impact of the influenza epidemic of 1918 and finds that cohorts in utero during the peak of the epidemic had six percent lower income and were 1.5 percent more likely to be in poverty than those born just prior to the epidemic. Subsequent investigations have found both larger and smaller effects. Richard E. Nelson (2009) finds considerably larger effects of exposure to the 1918 epidemic using data from the Brazilian monthly employment survey. Those exposed in utero were 17.2 percent less likely to be employed than those in surrounding cohorts, 7.2 percent less likely to be literate and 22.9 percent less likely to graduate from college. Elaine Kelly (2009) investigates the effects of the 1957 flu epidemic in Great Britain and finds that a one standard deviation increase in the severity of the epidemic reduced age 11 test scores of children who were in utero by 0.05 standard deviations.

She also finds effects on birth weight but only for the children of the least healthy mothers.

These varying findings suggest that the estimates do not only measure the biological impacts of flu – they also reflect the resources that were available to treat the afflicted, both at the time of the epidemic and afterwards.

Jessica Reyes (2005) examines the phase out of leaded gasoline in the U.S. and finds that it led to a three to four percent decrease in low birth weight and infant mortality. The effects are

15 identified using state-level variations in the timing of lead phase outs. It is difficult however to use available U.S. data to show evidence of a “first stage” in which the phase outs affected ambient lead levels. J. Peter Nilsson (2009) investigates the long-term impact of banning leaded gasoline in Sweden during the 1970s and is able to make the connection between phase outs and ambient lead using measures taken from moss samples. Identification comes from the fact that the ban had different impacts on different residential locations. One remarkable thing about his sample is that at the time of the phase-out, peak child blood levels in Sweden were already below the ten micrograms per deciliter that is the current “threshold for concern” in the U.S. so his results pertain to a level of lead emissions that is relevant for the U.S. today. His estimates imply that reducing levels from ten to five micrograms per deciliter would increase high school graduation rates by 2.3 percent and increase mean earnings between ages 20 and 32 by 5.5 percent. He finds larger effects on children of lower socioeconomic status even among those who grew up in the same neighborhood.

Nicholas J. Sanders (2010) builds on the work of Chay and Greenstone by asking whether high school students who were impacted by the reductions in pollution caused by the recession of the early 1980s have higher test scores as a result. Sanders finds that a one standard deviation decrease in Total Suspended Particles while the child was in utero is associated with an increase of 1.87 percent of a standard deviation in high school test scores.9

These studies illustrate the fact that health at birth is predictive of future outcomes. This relationship has been demonstrated in a wide range of settings and suggests that poor health at birth could be a cause of low socioeconomic status in adulthood. Moreover, direct estimates of

9 A caveat is that he does not actually observe where the child was in utero, and so must assume that they were born in the location of residence during high school. He proposes an instrumental variable based on a “shift-share” analysis projecting state-level changes in industrial composition to the local level based on initial local employment shares.

16 the impact of fetal health shocks indicate that estimates based on birth weight could understate

the magnitude of the harm.

d) The Intergenerational Transmission of Shocks to Health at Birth

While the papers discussed above emphasize that health at birth is important for an

individual’s outcomes, economists have also shown that a mother’s health at birth impacts her

child’s future health. For example, Dora L. Costa (1998) argues that much of the inequality in

birth weight observed over the course of the 20th century was due to differences in mothers’ early

health endowments.

The Currie and Moretti (2007) study discussed above uses a large sample of sisters drawn

from California birth certificates from the 1960s to the 1990s. Birth certificates record the

mother’s state of birth. For mothers who were born in California during that interval, it was

possible to go back and find the mother’s birth certificate, and to identify mothers who were

sisters. Thus, there is some information about both an infant’s birth weight and the mother’s

birth weight, and there is information about maternal circumstances at the time of her infant’s

birth, as well as at the time of her own birth.

Sister comparisons using this data set show that women who were low birth weight are more likely to deliver low birth weight infants, and this effect is greater if the women are living in a low income neighborhood. What these results indicate is that like height, low birth weight is transmissible for reasons that are not purely genetic, since low adult socioeconomic status compounds the negative impact of maternal low birth weight, and makes it more likely that the

child will also be low birth weight.

17 It has been known for some time from animal studies that environmentally induced changes in the epigenome can be transmitted from parents to offspring. For example, R. J. C.

Stewart, et al. (1980) starved pregnant rats and found that it took several generations for the descendants of the starved rats to return to the size of the control, non-starved rats even when all descendants shared the same diet.

The social science study that comes closest to suggesting that a similar mechanism might be at work in explaining the transmission of low birth weight is Almond and Chay (2006). They build on previous work showing that the Civil Rights movement had a large effect on the health of black infants in some southern states, especially Mississippi, due to the desegregation of hospitals and increased access to medical care (Almond, Chay, and Greenstone, forthcoming).

For example, there was a large decline in deaths due to infectious disease and diarrhea in these cohorts. Because birth records include the mother’s state of birth, it is possible to identify black women who benefited from these changes (the 1967 to 1969 cohorts) regardless of their state of residence as adults, and to compare the outcomes of their infants to the outcomes of infants born to black women in the 1961 to 1963 birth cohorts. The birth outcomes of white women in the same cohorts are examined as a control. Almond and Chay (2006) conclude that the infants of black women who had had healthier infancies as a result of the Civil Rights movement show large gains in birth weight relative to the infants of black women born just a few years earlier, and that these gains are largest for women from Mississippi – the most affected state.

To summarize, there are large inequalities in health at birth. Despite the stability of these inequalities over time, we know that health at birth is amenable to a range of interventions.

Moreover, health at birth has a causal impact on a wide range of outcomes both among affected

18 individuals, and among their children, though the extent to which it does so is itself mediated by

environmental factors.

II. Health at Birth and Environmental Justice

The preceding discussion suggests that many differences that appear to be innate may in

fact be the product of environmental factors. The papers reviewed above suggest that residential

location may be a key determinant of an infant’s environment. For example, we saw that high

poverty neighborhoods, proximity to toxic releases, and proximity to toll plazas were all

associated with worse health at birth.

One version of the Environmental Justice hypothesis holds that minorities are more likely

to be exposed to pollution because new pollution sources are more likely to be located in

minority neighborhoods, or because cleanups are more likely to occur in affluent neighborhoods.

However, recent investigations by environmental economists (Shreekant Gupta, George Van

Houtven, and Maureen Cropper, 1995; W. Kip Viscusi and James T. Hamilton, 1999; Hilary

Sigman, 2001) show little support for this hypothesis. For example, a recent paper examining reductions in nitrogen oxide emissions in response to a mandated program found no evidence that changes in emissions varied with neighborhood demographic characteristics (Meredith

Fowlie, Stephen P. Holland, and Erin T. Mansur, 2009). Wayne B. Gray and Ronald J.

Shadbegian (2002) actually find that plants with more non-whites nearby emit less pollution.

Instead, the economics literature implicitly suggests that if poor and minority children are more likely to be exposed to pollution, then this may be because their parents are less likely to move away from environmental hazards. If pollution depresses housing prices, polluted neighborhoods may become more attractive to poor families. Alternatively, perhaps some

19 groups are less able to process and act on information about hazards. There have been surprisingly few attempts to test these conjectures directly by looking at residential mobility.

Some tests along these lines are discussed below. A theme that emerges is that the answers to

these questions are clearer when one has access to continuous data at a fine level of geographical

disaggregation.

This section addresses two hypotheses arising from the Environmental Justice literature.

The first question is whether the children of minority and less educated mothers are, in fact, more

likely to be exposed to pollution in utero? There is a large literature outside of economics that addresses this issue. Many of these studies focus only on a particular town or area, so that it is

difficult to generalize or to see regional patterns. By using individual-level data on millions of births from five large states, and the mother’s exact residential location, I will address this question with more precision than has been possible previously.

The data set used here combines individual-level information about 11 million births that took place in five large states (Florida, Michigan, New Jersey, Pennsylvania, and Texas) between

1989 and 2003 with information about two sources of pollution: Superfund sites and facilities listed in the Environmental Protection Agency’s Toxic Release Inventory (TRI). Given that the

birth data includes the mother’s residential address, it is possible to calculate her distance from a

Superfund or TRI site in meters. The sample is restricted to singleton births.10

In 1980, the outcry over the health effects of toxic waste in Love Canal, New York

resulted in the Comprehensive Environmental Response, Compensation, and Liability Act,

10 Multiple births are excluded because they are much more likely to have health problems. However, including multiple births does not alter the conclusions reported here.

20 which became known as Superfund. 11 Superfund was intended to provide a mechanism for

initiating clean-ups at the most dangerous hazardous waste sites. More than 1,500 sites are

eligible for clean-up nationally; our sample includes 426 sites. The Superfund data is similar to

that analyzed in Greenstone and Justin Gallagher (2008), and in Currie, Greenstone, and Moretti

(2011).12

The Toxic Release Inventory was created by the Emergency Planning and Community

Right to Know Act (EPCRA) in 1986, in response to the Bhopal disaster and a series of smaller

spills of dangerous chemicals at Union Carbide plants in the U.S. Bhopal added urgency to the

claim that communities had a “right to know” about hazardous chemicals that were being used or

produced in their midst. EPCRA required manufacturing plants (those in Standard Industrial

Classifications 2000 to 3999) with more than 10 full-time employees that either use or produce more than threshold amounts of listed toxic substances to report releases to the EPA for public disclosure.13

11 Love Canal is a neighborhood in Niagara Falls, New York which became notorious when it was discovered that the neighborhood (including a school) was built on top of 21,000 tons of dangerous chemical wastes, including dioxin. Construction activity at the site released the wastes leading to severe health problems. Eventually, the federal government stepped in and evacuated the residents. 12 The data were downloaded from the EPA's Superfund Information Systems website (http://cfpub.epa.gov/supercpad/cursites/srchsites.cfm) on 9/17/2008. Each NPL site has its own webpage. The data on these web pages were parsed from HTML to comma-separated value format using a custom Python script. The initiation of the cleanup was coded using the starting date of the first “Remedial Action” after a “Record of Decision.” The site’s clean-up completed date was coded as the date for “Construction Complete.” If no such date was listed, then the site was considered to be pre-completion. 13 Plants are required to file a separate form for each substance and must identify whether the release was to ground, water, or air. Like Currie and Schmeider (2009), I focus on air-borne releases because people living close to a plant may be more likely to be exposed to them than to water or ground releases. The previous calendar year’s toxic releases are required to be reported by July 1. Several studies have examined compliance to the reporting regulations and data quality (see Gerald V. Poje and Daniel M. Horowitz, 1990; John Brehm and Hamilton, 1996; Thomas E. Natan and Catherine G. Miller, 1998; Scott de Marchi and Hamilton, 2006; Dinah A.

21 A possible drawback to using data on births is that pollution could affect the probability

of a conception or of a live birth. If we suppose that pollution abatement would lead to fewer fetal deaths, and more births, and that the marginal fetus lost due to pollution is more vulnerable

and less healthy than others, then focusing on births will tend to understate the beneficial effects

of abatement by increasing the number of less healthy infants whose birth weight is recorded. It

is difficult to look at fetal deaths directly given that they are not required to be reported in most

states until after 20 weeks, whereas most fetal losses occur in the first trimester of pregnancy.

Hence, the results below should be interpreted keeping this potential source of downward bias in

mind.

Table 2 shows that in our five-state sample of births, non-whites are in fact much more

likely to live within 2000m of a TRI or Superfund site. There is also a gradient by education

within race, though it is much smaller. It is conceivable that these raw differences reflect other

characteristics that are correlated with both race/ethnicity and residential location. For example,

it is not uncommon for studies of Environmental Justice to find that counties with manufacturing

plants emitting toxic releases are both more heavily African-American and higher income, a

finding which may just reflect the fact that these counties also tend to be more urban.

Table 3 shows estimates from linear probability models of the form:

(1) Pr(live<2000m facility) = β0 + β1X + β2Zip + β3Year + ε,

where the dependent variable is whether an individual mother lives within 2000 meters of a

facility. The vector X includes controls for the mother’s race/ethnicity, mother’s education (less

than high school, high school, some college, college or more), mother’s age (less than 20, 20 to

Koehler and John D. Spengler, 2007). While these papers point to some under-reporting, overall compliance was high and changes in reported releases correspond to changes in plant operations and production levels.

22 24, 25 to 29, 30 to 34, 35 to 39 and 40 or over), parity (1st, 2nd, 3rd, 4th, or higher order birth), and

child gender. When control variables are missing, the regressions include indicators for missing

values. The models include zip code fixed effects to control for other characteristics of the

location as well as indicators for each year of birth: Hence, these models ask whether within zip codes, minority mothers and/or less educated mothers are more likely to live near TRI or

Superfund sites. Standard errors are clustered at the county-year level to allow for correlations within those cells.

The first column of Table 3 shows the probability of living near a site falls with education but is roughly 40 percent higher for African-American women than for others. It is interesting that conditional on the other factors, smokers are more likely to live near a Superfund site, perhaps indicating a role for tastes.

Column 2 shows that the same patterns hold but are stronger for residence near a TRI site. Overall, almost half the sample lives within 2000m of a TRI site. Even within zip code, the college educated are 3.8 percentage points less likely to live close to a site, while African-

American and Hispanic women are 5.3 and 4.0 percentage points more likely to live close to a site, respectively. Smokers are also more likely to live close to TRI sites. It is striking that older

mothers are much less likely to live close to a TRI site.14

These estimates strongly support the claims of the Environmental Justice literature that

minorities and people of lower socioeconomic status are more likely to be exposed to potentially

harmful pollutants for reasons that cannot be explained by their broad geographical distribution,

14 This could be partially an effect of income given that income is not available in the data and hence is not included in the regressions. But since the models do control for zip code, the difference between a high income and a low income zip code is controlled. It may also be a function of tastes: It is possible that women who give birth at older ages are more cautious about environmental hazards.

23 education, or other observable characteristics. They suggest that data collected at a fine enough

level of disaggregation can deliver a clear answer to this question.

b) Voting with the Feet

A second question that can be addressed with these data is whether mothers move when

there are changes in environmental conditions. A large number of studies have investigated this

question using hedonic models of housing prices.15 For example, Chay and Greenstone (2005)

use a discontinuity created by the implementation of the Clean Air Act as an instrument for

reductions in air pollution between 1970 and 1980. They find that the large reductions in

pollution that were mandated in counties that were out of compliance with pollution standards

were capitalized into housing prices.

However, the estimated effects of amenities on housing prices often vary considerably

from study to study (V. Kerry Smith and Ju-Chin Huang, 1995). For example, Linda T. M. Bui

and Christopher J. Mayer (2003) found no effect of toxic releases on housing prices at the zip

code level. But Felix Oberholzer-Gee and Miki Mitsunari (2006) find that average housing

prices fell after the TRI data was first released in June 1989, and that they fell most in houses less than a half mile from an emitting plant.

Limitations of housing price data and hedonic models of housing prices make it useful to look more directly at whether residents “vote with the feet” following changes in neighborhood amenities (Charles M. Tiebout, 1956). One obvious limitation is that housing prices are only observed when houses are sold. Hence, if a disamenity is such that it results in unsold or even abandoned housing, it may be difficult to see this in the housing price data. Second, data on

15 See Smith and Huang (1995) and Nicolai V. Kuminoff, Smith, and Christopher Timmins (2010) for surveys of this literature.

24 housing sales often has only limited information about the characteristics of the structures,

making it difficult to ascertain that “treatment” housing units are similar to “controls.” Third, the

people most affected by environmental disamenities may be renters rather than owners, so that

their transactions are not observed.

A more subtle point given the literature on “tipping” (see David Card, Alexandre Mas,

and Jesse Rothstein, 2008) and on the way that people of different racial or ethnic groups sort

across neighborhoods (Patrick Bayer, Fernando Ferreira, and Robert McMillan, 2007) is that it is

possible for the character of a neighborhood to change considerably even without a change in

housing prices. For example, Bayer, Ferreira, and McMillan (2007) conclude that consumer preferences for racial segregation can generally be accommodated without the need for price differences to clear markets.

A handful of previous studies have conducted direct investigations of the relationship between environmental amenities and mobility using decennial Census data. Trudy Ann

Cameron and Ian T. McConnaha (2006) examine changes in demographic characteristics around

four Superfund sites at various stages of cleanup. Using data on the Census tract level, they do

not find consistent patterns across sites, however. Greenstone and Gallagher (2008) also use data

at the Census tract level and find little change in the demographic composition of tracts in

Census years before and after Superfund cleanups.

H. Spencer Banzhaf and Randall P. Walsh (2008) overlay California with ½ mile

diameter circles. Focusing on circles that had a TRI facility, they then show that areas in which

releases increased (or decreased) experience losses (or gains) in population between 1990 and

2000. The main potential difficulty is that other things might have changed in these

25 neighborhoods between 1990 and 2000—for example, plant shutdowns would reduce pollution,

but might also reduce employment opportunities.

In this section I first ask whether there is any evidence of maternal sorting in response to

Superfund cleanups. The Vital Statistics Natality Data offers a complete census of births, which

is the relevant population for examining infant health, and the fact that it is continuous is a great

advantage over decennial Census data. As discussed above, I can also look at individual mothers

in a given radius of a Superfund site, rather than using means taken over an arbitrary geographic

unit.

In order to examine the effects of cleanups, I estimate difference-in-differences models of

the form:

(2) Pr(Maternal Characteristic) = β0 + β1Close + β2After + β3During + β4Close*(During

Cleanup) + β5Close*(After Cleanup) + β6Zip + β7Year + ε,

where the dependent variable is the probability that a mother belongs to a particular demographic

group (such as “white college educated”), the sample is restricted to people within 5000m of a

Superfund site and “Close” is defined as being within 2000m of a Superfund site. “During

Cleanup” means that a cleanup was initiated but was not yet complete, and “After Cleanup”

indicates that the cleanup was complete.16 Zip and Year are defined as in model (1). Here

timing refers to the date of conception, and children conceived more than 4 years prior to the

start of a cleanup, or more than 4 years after the completion of a clean up are excluded in order

to focus on children in a relatively narrow window around Superfund cleanups. Standard errors are clustered at the county-year level to allow for correlation in the errors at that level.

16 The average Superfund site in our sample is 495 acres or about 2 square kilometers.

26 The key coefficient of interest is β5 which can be interpreted as measuring the extent to

which the area surrounding a Superfund site became “whiter” (for example) after a cleanup.17

An area could become whiter due to an appreciation in housing values following a site cleanup

(Shanti Gamper-Rabindran and Timmins, 2011). But this is not the only possible mechanism. It is possible that different groups place different values on environmental amenities, on average.

Finally, it is possible that the removal of one disamenity, makes other characteristics of the neighborhood more salient and that these characteristics are valued differently by different groups (as in Banzhaf and Walsh, 2010).

The difference-in-difference formulation controls for other things that might be expected to occur both in the immediate vicinity of a site, and a little further away. For example, if white college educated women have lower fertility on average than other groups, this should be true both within 2000m of a Superfund site and between 2000 and 5000m of a site. A potential objection to the model is that the probability of cleanup may be related to characteristics of mothers near the site. However, consistent with the previous literature on the subject cited above

I find little evidence that this is the case.

Estimates from equation (2) are shown in Table 4. Here, each column includes estimates from a separate regression. The first panel shows estimates for all cleanups, while the second panel considers only cleanups of sites in the top third of the distribution of hazardous risk assessment scores (HRS). The estimates of β5 show that Superfund cleanups have a significant

effect in the equation for “white college educated” mothers: Mothers in the immediate vicinity of

a Superfund site are more likely to be white and college educated following cleanups. This

17 The expected sign of β2 is ambiguous. On the one hand, during the cleanup people might look forward to living in a cleaner neighborhood or foresee rising property values as a result of the cleanup that is in process. On the other hand, the active cleanup may itself entail inconveniences for residents, and is an active reminder of the presence of the Superfund site.

27 effect is larger for the most dangerous sites, and the estimated effect of cleanup also becomes

statistically significant at the 90% level of confidence in the equation for all white mothers, indicating that areas surrounding the most dangerous Superfund sites do become “whiter” following site cleanup. The coefficients on cleanup in the equations for black mothers are negative though not statistically significant, suggesting that there may be an offsetting loss of black residents following cleanup, though I do not have the power to detect it. Overall, the

results suggest educated, white mothers are more likely than other groups to respond to

Superfund cleanups.

It is possible that education matters because it helps people to process information. The pure effect of information can be assessed by exploiting changes in the TRI program’s reporting requirements. In 1999 reporting thresholds were dramatically lowered for persistent

bioaccumulative toxins (PBTs). The amounts triggering reporting requirements fell from 10,000

pounds to 100 or 10 pounds (or to .1 gram in the case of dioxin). These new reports were due in

2001 and first became available in June 2002. This change in reporting requirements led 609

factories to begin to report that they emitted PBTs. In most cases, these factories were already

reporting that they emitted other chemicals. Hence, mobility responses to the new PBT reporting

requirements shed light on the extent to which people who presumably already know that they live close to a plant emitting toxic chemicals respond to the announcement that the plant emits a

particularly dangerous chemical, such as arsenic or mercury.18

18 The interpretation of other changes in reporting requirements is less clear. For example, in 1998, seven new SIC categories were added to the list of facilities required to report (metal mining, coal mining, electric utilities, petroleum bulk terminals, chemicals wholesalers, RCRA commercial hazardous waste treatment, and solvent recovery). People living near these facilities did not previously have any way of knowing what they emitted and could conceivably have been reassured by the reports. Newly included companies filed their first reports in 1999, and the data were released to the public in 2000. I do not find a significant effect of this change on the

28 Table 5 reports estimates from difference-in-difference-in-difference models that exploit

comparisons between those who are closer or farther from a TRI plant; between TRI plants that

were affected by the new PBT regulations and those that were not; and between births before and

after the first announcements were made. These models take the following form:

(3) Pr(Maternal Race/Ethnicity) = β0 + β1Close + β2PBT + β3After + β4Close*PBT +

β5Close*After + β6PBT*After + β7PBT*Close*After + β8Zip + β9Year + ε,

where PBT indicates that the TRI site was affected by the new regulations, and After denotes a time after the first announcement has been made (since the announcements were made in the

middle of the year, “after” is not entirely collinear with the year dummies). The sample is

restricted to those within 4000m of a TRI site. The other variables are defined as in model (2).

The model is estimated using data from 1995 to 2003.

Table 5 confirms that white mothers, and especially white college educated mothers are less likely to live near a TRI plant than other mothers, while black and Hispanic mothers are more likely to live near these plants. Prior to the new announcement about PBTs, white women were more likely than other women to live near plants that would be affected by the requirement.

One way to interpret this result is that, conditional on living within 4000m of a TRI plant, white

women were more likely to live near a plant that was emitting PBTs at levels less than the old

10,000 pound threshold, but higher than the new threshold. The coefficient β5 on “Close*After” is positive for white women and negative for black women, suggesting that over time, white women were more likely to live near TRI plants, while black women were less likely to live

composition of mothers in the neighborhood of the new reporters. If the PBT plants and the new SIC plants are combined, the results are similar to those reported here for the PBT plants. Thresholds for reporting lead were lowered to 100 pounds in 2001, with the first new reports becoming available to the public in June 2003. Since the data set currently ends in 2003, there is not enough of an “after” period to examine this change.

29 nearby. This pattern may be explained by the reduction in TRI emissions that has occurred over

time, which has made such areas more attractive places to live (see Banzhaf and Walsh, 2008;

Currie and Schmeider, 2009).

Estimates of the key coefficient β7 on the triple interaction (PBT*Close*After) are

statistically significant only for white college educated women and suggest that there was a large

reduction in the probability that mothers in this category lived near the PBT emitters following

disclosure of this information. The coefficient estimate suggests an 8.7 percent reduction in the

fraction of mothers near such plants relative to the population of white college educated mothers

living within 4000m of a TRI plant.

Is such a large effect of information credible?19 The EPA posts information from the TRI

on its web site, but it can be difficult to use and to interpret. However, many organizations

synthesize and publicize data from the TRI. For example, organizations such as Scorecard: The

Pollution Information Site (http://www.scorecard.org/env-releases/community.tcl, 2005) and the

Public Interest Research Group (see Abigail Caplovitz Field, 2007) process and disseminate

information from the TRI widely. And many state governments also disseminate information

from the TRI (Hyunhoe Bae, Peter Wilcoxen, and David Popp, 2008). Moreover, it is plausible

that the largest effects of information (and indeed the only statistically significant effects) should

be on those with more education (Jay P. Shimshack, Michael B. Ward, and Timothy K. M.

Beatty, 2007). Finally, the results are consistent with those for the Superfund cleanups, where I

19 Experiments aimed at evaluating the impact of giving consumers financial information typically have shown small effects on things like decisions about retirement plans (see Esther Duflo and Emmanuel Saez, 2003; John Beshears, et al., 2009; Marianne Bertrand and Adair Morse, 2009). But mothers may feel more strongly about fetal health than about their retirement plans.

30 also found larger effects for white college educated women than for others. These results

suggest yet another channel through which maternal college education can affect infant health.

The preceding discussion raises some important questions. First, how much of the

persistent gap in health at birth is likely to be explained by exposure to environmental toxicants?

Answering this question is difficult given the rudimentary state of knowledge regarding the

health effects of toxicants. For example, there are many chemicals included in the TRI for which the EPA has no risk score, and/or for which no information exists about whether the chemical is harmful to the human fetus. Even for criterion air pollutants such as CO, until recently little attention had been paid to the possible fetal health effects of ambient levels below the current

EPA thresholds for concern.

Currie, Lucas Davis, and Walker (2011) use TRI plant closures as an instrument for releases and find that among mothers within 2000m of a plant, a two standard deviation reduction in pollution reduces the incidence of low birth weight by approximately .02. Given the ten percentage point gap in the incidence of low birth weight between white college educated mothers and black high school dropout mothers, if all of the latter and none of the former lived near a plant, and if we assume that toxic releases increase LBW by .02 for those near the plant and have no impact on those more than 2000m from the plant, then exposure to toxic releases could potentially explain a fifth of the gap between the two groups.

Of course, actual sorting is less extreme. If we maintain the assumptions about the relative magnitudes of the effects of toxic releases on those closer and further from a plant, and use the actual distributions of locations shown in Table 2, then the estimates suggest that six

percent of the gap in low birth weight between white college educated mothers and black high

31 school dropout mothers could be attributed to differences in exposures from TRI plants.20 This is a large enough share to suggest that while releases from TRI plants are not a “smoking gun” that explains the differential, they could be an important factor. Moreover, air-borne emissions from

TRI plants are not the only source of toxic exposures for the fetus and may not be the most important. For example, there are many potential sources of toxicants within the household such as tobacco smoke (as discussed above), plasticizers, and pesticides (see Virgina A. Rauh, et al.,

2006).

A second question concerns the incidence of environmental policies. If educated white mothers respond to environmental remediation policies and others do not, then place-based policies may exacerbate inequalities. If an area “gentrifies” in response to environmental policies, then this will tend to benefit property owners but may harm renters. Clearly, as Ian W.

H. Parry, et al. (2006) and Don Fullerton (2008) emphasize, it is important to understand the incidence and distributive effects of environmental policies and there is relatively little known about these issues. It may be that person-based policies such as WIC, are more equitable and effective remediation tools than place-based policies.

III Conclusions and Directions for Future Research

This essay shows that inequality begins well before school age, and indeed, before birth, and that large differences in health at birth have important consequences for future outcomes.

What is just as important, however, is to understand the malleability of health at birth. Many interventions have been demonstrated to either help or harm children while they are still in the

20 For white college educated mothers, .3725x + .6275(x-.02)=.038 where x is the incidence of LBW in the absence of toxic releases. For black mothers with less than high school education .6701x + .3299(x-.02)=.138. Hence, in the absence of toxic releases the rates of LBW would be .0306 and .1246 respectively, and the gap would be .0940 rather than .1.

32 womb. Hence, we cannot assume that differences that are present at birth reflect unchangeable, genetic factors. The new science of epigenetics makes it clear that they are much more likely to reflect interactions between “nature” and “nurture.”

The fact that interventions can change health at birth does not mean that it will be easy to address sources of inequality at birth. As this essay has shown with an extended example, economic actors may tend to undo the effects of policy. This may be particularly likely when more advantaged people grasp the benefits of the policy more readily than those who are less advantaged.

Future research should be aimed at identifying the most important sources of inequalities at birth. This essay suggests that differential exposure to environmental toxicants is an important factor, but only one among many. Research must also evaluate the effectiveness and distributional consequences of policies intended to address inequalities at birth. For example, higher education for mothers appears to have beneficial effects on infant health through multiple pathways but there is great controversy about the best ways to improve educational attainment.

Moreover, policies designed to encourage education may reach a different part of the distribution of potential mothers than policies aimed at improving nutrition or health care among pregnant women.

Clearly much work remains to be done documenting the consequences of inequality at birth and improving our understanding of its consequences over the lifecycle; investigating the effects of policy, including the possible countervailing actions of economic agents; and, in the spirit of Richard Ely, asking: what types of interventions are most likely to give children “a fair start in life?” This is an exciting research agenda, and one that is still in its infancy!

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42 Table 1: Summary Statistics for Demographic Groups: 1989 and 2006 Singleton Births Only

Low Birth Weight Premature (<2500g) (<37 weeks) Mean SD Mean SD YEAR: 1989 All Mothers 0.060 0.237 0.097 0.296 White Mothers 0.045 0.208 0.072 0.258 Black Mothers 0.137 0.344 0.191 0.393 Hispanic Mothers 0.058 0.233 0.110 0.313 White Mothers with College or More 0.032 0.176 0.058 0.234 White Mothers with

Notes: For birth outcomes (LBW rates, premature rates), the units of analysis are individual birth records for all singleton births in 1989 and 2006. There were 3,948,042 singleton births in 1989 and 4,121,898 in 2006. Table 2: Fraction within 2000 meters of a Toxic Release Inventory or Superfund Site TRI Superfund Mean SD Mean SD All Mothers 0.4691 0.4990 0.0174 0.1310 White Mothers 0.4051 0.4909 0.0159 0.1250 Black Mothers 0.6121 0.4873 0.0298 0.1701 Hispanic Mothers 0.5153 0.4998 0.0137 0.1161 White Mothers with College or More 0.3725 0.4835 0.0114 0.1190 White Mothers with

Notes: Sample is singleton births from 1989 to 2003 in FL, MI, NJ, PA, and TX. For Superfund, births conceived after the initiation of a cleanup are omitted. There are 11.4 million observations in the TRI sample, and 9.2 observations in th Superfund sample. Table 3: Within Zip Code Differences in Maternal Characteristics between those within 2000m and those further from a Superfund or Toxic Release Inventory site Toxic Characteristics Superfund Releases High School Graduat -0.0006 -0.0115 [0.0005] [0.0006]** Some College -0.0015 -0.0214 [0.0004]** [0.0008]** College Graduate -0.0032 -0.0375 [0.0005]** [0.0011]** Black 0.0074 0.0532 [0.0019]** [0.0019]** Hispanic 0.0003 0.0398 [0.0012] [0.0011]** Smoker 0.0012 0.0117 [0.0003]** [0.0006]** Age 20-24 0.0006 0.0021 [0.0003]* [0.0005]** Age 25-29 0.0006 0.007 [0.0004] [0.0006] Age 30-34 -0.0004 -0.0193 [0.0005] [0.0008]** Age 35-39 -0.0006 -0.0256 [0.0010] [0.0009]** Age 40+ -0.001 -0.0282 [0.0009] [0.0011]** Constant 0.0323 0.4815 [0.0062]** [0.0014]** R-squared 0.307 0.507 #Obs. 9,169,128 11,410,768

Notes: All regressions include zip code fixed effects and year fixed effects as well as indicators for whether the child is male, parity, and indicators for missing values of the controls. All samples include only singleton births between 1989 and 2003 in FL, MI, NJ, PA and TX. For Superfund, births conceived after the initiation of a cleanup are omitted. A ** indicates significance at the 95% level of confidence. A * indicates significance at the 90% level of confidence. Table 4: Effects of Superfund Cleanups on Characteristics of Mothers Within 2000m of a Site [1] [2] [3] [4] [5] White Black Characteristic: White College Black < HS Hispanic Full Sample Close -0.0136 -0.0466 0.0238 0.0053 0.0078 [0.0136] [0.0066]** [0.0128] [0.0059] [0.0127] During*Close 0.0125 0.0102 -0.0143 -0.0042 -0.0009 [0.0103] [0.0060] [0.0102] [0.0046] [0.0096] After Cleanup* 0.0034 0.0147 -0.0148 -0.0016 0.0060 Close [0.0148] [0.0069]** [0.0134] [0.0060] [0.0119] Mean Dep. Var. 0.7688 0.1454 0.2168 0.0639 0.1535 R-squared 0.4149 0.1768 0.4346 0.1375 0.3665

Top HRS Sites Close -0.0214 -0.0455 0.0348 0.0136 0.0044 [0.0249] [0.0085]** [0.0287] [0.0126] [0.0297] During*Close 0.0320 -0.0001 -0.0191 -0.0107 0.0073 [0.0171] [0.0096] [0.0210] [0.0082] [0.0214] After Cleanup* 0.0395 0.0232 -0.0370 -0.0089 0.0145 Close [0.0235]* [0.0112]** [0.0283] [0.0116] [0.0283] Mean Dep. Var. 0.7486 0.1350 0.1850 0.0490 0.2218 R-squared 0.4248 0.1538 0.3818 0.1172 0.3829

Notes: Standard errors are clustered at the county-year level and appear in brackets. Only singleton births conceived between 4 years prior to the initiation of a cleanup and 4 years after completion are included. Only births within 5000m of a site are included. "Close" is defined as within 2000 meters of the site. Regressions control only for year of birth and zip code. There are 618,726 observations in the first panel and 267,852 observations in the second panel. A ** indicates that the estimate is significant at the 95% level of confidence, a * indicates significant at the 90% level. Table 5: Effects of New Report that the Closest TRI Site Emits PBTs on Characteristics of Mothers Within 2000m of a Site

[1] [2] [3] [4] [5] White Black Characteristic: White College Black < HS Hispanic Close to Any TRI -0.0490 -0.0395 0.0227 0.0110 0.0271 [0.0026]** [0.0019]** [0.0027]** [0.0010]** [0.0025]** Any New PBT Reporter 0.0125 0.0064 -0.0113 -0.0011 0.0014 within 4000m [0.0061]** [0.0049] [0.0043]** [0.0018] [0.0037] Close*New PBT Reporter -0.0026 -0.0026 0.0013 -0.0024 0.0032 [0.0059] [0.0038] [0.0060] [0.0019] [0.0036] After*Close 0.0087 0.0053 -0.0091 -0.0048 0.0006 [0.0032]** [0.0024]** [0.0029]** [0.0012]** [0.0026] After*New PBT Reporter 0.0051 0.0111 -0.0009 -0.0016 -0.0024 [0.0092] [0.0051]** [0.0058] [0.0022] [0.0085] After*Close -0.0064 -0.0113 0.0013 0.0036 0.0028 *New PBT Reporter [0.0100] [0.0056]** [0.0079] [0.0031] [0.0080] Mean Dep. Var. 0.4900 0.1570 0.1960 0.0530 0.2720 R-squared 0.4132 0.1893 0.3893 0.1308 0.4053 # Obs. 5.06 mill

Notes: Standard errors are clustered at the county-year level and appear in brackets. Only singleton births between 1995 and 2003 are included. Only births within 4000m of a site are included. "Close" is defined as within 2000 meters of the site. Regressions control for year of birth, zip code, and a dummy for "after" the announcement since the announcement occurs in the middle of the year. A ** indicates significance at the 95% level of confidence. A * indicates signifcance at the 90% level. Figure 1 Correlation between Early Childhood Conditions and Adult Earnings

(a) Wage Earnings at Age 24-27 vs. Mean Test Scores (b) Log of Wage Earnings at Age 24-27 vs. Mean Test Scores 10.5 40000 10 30000 9.5 20000 Mean Wage Earnings (24-27) Earnings WageMean (24-27) Earnings WageMean 9

10000 0 20 40 60 80 100 0 20 40 60 80 100 Mean of PPVT & Piat Scores Mean of PPVT & Piat Scores

(c) Wage Earnings at Age 24-27 vs. Birth Weight (d) Log of Wage Earnings at Age 24-27 vs. Birth Weight 10.5 40000 10 30000 9.5 20000 Mean Wage Earnings (24-27) Earnings WageMean Log of Mean Wage Earnings (24-27) Earnings WageMean of Log 9

10000 2 4 6 8 10 12 2 4 6 8 10 12 Birth Weight (lbs) Birth Weight (lbs)

Notes: Plots of correlations between mean wage earnings from age 24 to 27 (in levels and logs) and early childhood conditions. Mean wage earnings are measured as the average of all reported wage earnings at age 24, 25, 26, and 27 (including zero earnings). Log wage earnings are measured as the natural log of mean wage earnings (excluding zero earnings). Mean test scores are measured as the mean of all valid PPVT, Piat Math, and Piat Reading Recognition test scores up to, and including, age six. Birth weight is measured in pounds, as reported by mothers. For these figures, test scores are divided into five-point bins (0–5, 6–10, etc.), and birth weight is divided into half-pound bins. Each data point represents the mean outcome for all observations in that bin, with the size of the point is proportional to the number of observations in that bin. The dotted line represents the fitted values from an OLS regression of the earnings measure on the early childhood condition in the underlying data. Data are from the NLSY79 matched mother- child file. The sample is restricted to those born between 1980 and 1988 with valid observations for birth weight and PPVT and Piat test scores, at least one valid observation for wage earnings between age 24 and 27, and omitting those with very low and very high birth weights (<1,000 grams or >5,100 grams). The resulting sample sizes are 1,330 (panel a), 1,216 (panel b), 2,013 (panel c), and 1,825 (panel d).